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doc(python): document the method in fts (#982)
Co-authored-by: prrao87 <prrao87@gmail.com> Co-authored-by: Prashanth Rao <35005448+prrao87@users.noreply.github.com>
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@@ -75,21 +75,40 @@ applied on top of the full text search results. This can be invoked via the fami
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table.search("puppy").limit(10).where("meta='foo'").to_list()
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```
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## Syntax
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## Phrase queries vs. terms queries
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For full-text search you can perform either a phrase query like "the old man and the sea",
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or a structured search query like "(Old AND Man) AND Sea".
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Double quotes are used to disambiguate.
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For full-text search you can specify either a **phrase** query like `"the old man and the sea"`,
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or a **terms** search query like `"(Old AND Man) AND Sea"`. For more details on the terms
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query syntax, see Tantivy's [query parser rules](https://docs.rs/tantivy/latest/tantivy/query/struct.QueryParser.html).
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For example:
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!!! tip "Note"
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The query parser will raise an exception on queries that are ambiguous. For example, in the query `they could have been dogs OR cats`, `OR` is capitalized so it's considered a keyword query operator. But it's ambiguous how the left part should be treated. So if you submit this search query as is, you'll get `Syntax Error: they could have been dogs OR cats`.
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If you intended "they could have been dogs OR cats" as a phrase query, this actually
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raises a syntax error since `OR` is a recognized operator. If you make `or` lower case,
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this avoids the syntax error. However, it is cumbersome to have to remember what will
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conflict with the query syntax. Instead, if you search using
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`table.search('"they could have been dogs OR cats"')`, then the syntax checker avoids
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checking inside the quotes.
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```py
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# This raises a syntax error
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table.search("they could have been dogs OR cats")
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```
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On the other hand, lowercasing `OR` to `or` will work, because there are no capitalized logical operators and
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the query is treated as a phrase query.
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```py
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# This works!
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table.search("they could have been dogs or cats")
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```
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It can be cumbersome to have to remember what will cause a syntax error depending on the type of
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query you want to perform. To make this simpler, when you want to perform a phrase query, you can
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enforce it in one of two ways:
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1. Place the double-quoted query inside single quotes. For example, `table.search('"they could have been dogs OR cats"')` is treated as
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a phrase query.
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2. Explicitly declare the `phrase_query()` method. This is useful when you have a phrase query that
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itself contains double quotes. For example, `table.search('the cats OR dogs were not really "pets" at all').phrase_query()`
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is treated as a phrase query.
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In general, a query that's declared as a phrase query will be wrapped in double quotes during parsing, with nested
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double quotes replaced by single quotes.
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## Configurations
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@@ -24,6 +24,12 @@ pip install lancedb
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::: lancedb.query.LanceQueryBuilder
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::: lancedb.query.LanceVectorQueryBuilder
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::: lancedb.query.LanceFtsQueryBuilder
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::: lancedb.query.LanceHybridQueryBuilder
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## Embeddings
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::: lancedb.embeddings.registry.EmbeddingFunctionRegistry
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@@ -62,10 +68,22 @@ pip install lancedb
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## Integrations
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### Pydantic
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## Pydantic
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::: lancedb.pydantic.pydantic_to_schema
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::: lancedb.pydantic.vector
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::: lancedb.pydantic.LanceModel
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## Reranking
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::: lancedb.rerankers.linear_combination.LinearCombinationReranker
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::: lancedb.rerankers.cohere.CohereReranker
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::: lancedb.rerankers.colbert.ColbertReranker
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::: lancedb.rerankers.cross_encoder.CrossEncoderReranker
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::: lancedb.rerankers.openai.OpenaiReranker
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@@ -106,8 +106,8 @@ class Query(pydantic.BaseModel):
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class LanceQueryBuilder(ABC):
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"""Build LanceDB query based on specific query type:
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vector or full text search.
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"""An abstract query builder. Subclasses are defined for vector search,
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full text search, hybrid, and plain SQL filtering.
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"""
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@classmethod
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@@ -118,6 +118,22 @@ class LanceQueryBuilder(ABC):
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query_type: str,
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vector_column_name: str,
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) -> LanceQueryBuilder:
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"""
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Create a query builder based on the given query and query type.
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Parameters
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----------
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table: Table
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The table to query.
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query: Optional[Union[np.ndarray, str, "PIL.Image.Image", Tuple]]
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The query to use. If None, an empty query builder is returned
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which performs simple SQL filtering.
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query_type: str
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The type of query to perform. One of "vector", "fts", "hybrid", or "auto".
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If "auto", the query type is inferred based on the query.
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vector_column_name: str
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The name of the vector column to use for vector search.
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"""
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if query is None:
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return LanceEmptyQueryBuilder(table)
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@@ -636,6 +652,16 @@ class LanceEmptyQueryBuilder(LanceQueryBuilder):
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class LanceHybridQueryBuilder(LanceQueryBuilder):
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"""
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A query builder that performs hybrid vector and full text search.
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Results are combined and reranked based on the specified reranker.
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By default, the results are reranked using the LinearCombinationReranker.
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To make the vector and fts results comparable, the scores are normalized.
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Instead of normalizing scores, the `normalize` parameter can be set to "rank"
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in the `rerank` method to convert the scores to ranks and then normalize them.
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"""
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def __init__(self, table: "Table", query: str, vector_column: str):
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super().__init__(table)
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self._validate_fts_index()
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@@ -177,10 +177,18 @@ def test_syntax(table):
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table.create_fts_index("text")
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with pytest.raises(ValueError, match="Syntax Error"):
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table.search("they could have been dogs OR cats").limit(10).to_list()
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# these should work
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# terms queries
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table.search('"they could have been dogs" OR cats').limit(10).to_list()
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table.search("(they AND could) OR (have AND been AND dogs) OR cats").limit(
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10
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).to_list()
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# phrase queries
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table.search("they could have been dogs OR cats").phrase_query().limit(10).to_list()
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# this should work
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table.search('"they could have been dogs OR cats"').limit(10).to_list()
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# this should work too
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table.search('''"the cats OR dogs were not really 'pets' at all"''').limit(
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10
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).to_list()
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